Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/darrenhinde/openagentscontrol/context7npx skills add darrenhinde/OpenAgentsControl --skill context7git clone --depth 1 https://github.com/darrenhinde/OpenAgentsControlWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00068 | $0.00772 |
| Opus 5 | $0.00034 | $0.00386 |
| Sonnet 5 | $0.00014 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
context7 scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
This skill enables retrieval of current documentation for software libraries and components by querying the Context7 API via curl. Use it instead of relying on potentially outdated training data. How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context7
Overview
This skill enables retrieval of current documentation for software libraries and components by querying the Context7 API via curl. Use it instead of relying on potentially outdated training data.
Workflow
Step 1: Search for the Library
To find the Context7 library ID, query the search endpoint:
curl -s "https://context7.com/api/v2/libs/search?libraryName=LIBRARY_NAME&query=TOPIC" | jq '.results[0]'
Parameters:
libraryName(required): The library name to search for (e.g., "react", "nextjs", "fastapi", "axios")query(required): A description of the topic for relevance ranking
Response fields:
id: Library identifier for the context endpoint (e.g.,/websites/react_dev_reference)title: Human-readable library namedescription: Brief description of the librarytotalSnippets: Number of documentation snippets available
Step 2: Fetch Documentation
To retrieve documentation, use the library ID from step 1:
curl -s "https://context7.com/api/v2/context?libraryId=LIBRARY_ID&query=TOPIC&type=txt"
Parameters:
libraryId(required): The library ID from search resultsquery(required): The specific topic to retrieve documentation fortype(optional): Response format -json(default) ortxt(plain text, more readable)
Examples
React hooks documentation
# Find React library ID
curl -s "https://context7.com/api/v2/libs/search?libraryName=react&query=hooks" | jq '.results[0].id'
# Returns: "/websites/react_dev_reference"
# Fetch useState documentation
curl -s "https://context7.com/api/v2/context?libraryId=/websites/react_dev_reference&query=useState&type=txt"
Next.js routing documentation
# Find Next.js library ID
curl -s "https://context7.com/api/v2/libs/search?libraryName=nextjs&query=routing" | jq '.results[0].id'
# Fetch app router documentation
curl -s "https://context7.com/api/v2/context?libraryId=/vercel/next.js&query=app+router&type=txt"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 85 lines · 68 tokens per session scan A 03c54428be33
context7 is a skill published in the GitHub repository darrenhinde/OpenAgentsControl (4,799 stars, last pushed 17d ago), licensed MIT. It adds 68 tokens to every session and 772 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
azure-openai-to-responses
Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API…
knowledge-base
Create and maintain a Markdown knowledge base that any AI agent can read, search, and update. Use when the user wants to start a knowledge base, add or update notes, organize docs/notes for an agent or LLM to consume, build an index of notes, or run a cleanup/maintenance pass on an existing MD knowledge base. Triggers…
peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…
statistical-power
Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers…
build-teaql-app
Build or change a TeaQL application in Java, Rust, Go, Swift, Python, C#/.NET, or TypeScript, including Kotlin/JVM applications that consume Java-generated libraries. Mandatory order: first draft and save a complete KSML model, then verify the client and evaluate that saved model, repair it through repeated evaluation…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.